In [1]:
#OMNS
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import os, glob, time
import cv2
import pickle
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from mpl_toolkits.mplot3d import Axes3D
from skimage.feature import hog
from scipy.ndimage.measurements import label
# NOTE: the next import is only valid for scikit-learn version <= 0.17
# for scikit-learn >= 0.18 use:
# from sklearn.model_selection import train_test_split
from sklearn.cross_validation import train_test_split
%matplotlib inline
####### Parameters/Controls ##############################################
DBG_PRINT = 0
DBG_SAVE_IMAGES = 2
data_folder = './train_data/*/*/*/'
test_img_folder = './test_images/'
#########################################################################
print ("***Done***")
In [8]:
'''
### Image transofrmations & scale (Data_format)
mpimg.imread(jpeg) -> RGB (0, 255) -> cv2.cvtColor() -> (0, 255)
cv2.imread(jpeg) -> BGR (0, 255) -> cv2.cvtColor() -> (0, 255)
mpimg.imread(png) -> RGB (0, 1) -> cv2.cvtColor() -> (0, 255)
cv2.imread(png) -> BGR (0, 255) -> cv2.cvtColor() -> (0, 255)
feature_image: unit8
Spatial Features: uint8
Color Hist Features: int64
HOG_features: float64
Img_Features[0:2]: uint8 int64 float64 (Spatial, Color Hist, HOG)
'''
################# DEFINE FUNCTIONS USED IN THE PROJECT ###########################
### Function to plot images
def plot_images(img_list, cmap_gray, title_list, top=0.8, right=1.0, imgs_per_row=2, \
figsize=(24,9), fontsize=20, fname='P5_output_image'):
nrows = -(-len(img_list)//imgs_per_row)
ncols = imgs_per_row
f, axes = plt.subplots(nrows, ncols, figsize=figsize)
f.tight_layout()
#plt.subplots_adjust(left=0., right=right, top=top, bottom=0., hspace=0.05)
#plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0., hspace=0.25, wspace=0.1)
idx = 0
if len(img_list) > 1: axes_array = axes.flatten()
else: axes_array = [axes]
for axis in axes_array:
if cmap_gray[idx]==1: axis.imshow(img_list[idx], cmap='gray')
elif cmap_gray[idx]==2: axis.imshow(img_list[idx], cmap='hot')
else: axis.imshow(img_list[idx])
img_title ="'"+title_list[idx]+"'"
axis.set_title(img_title, fontsize=fontsize)
idx=idx+1
if (DBG_SAVE_IMAGES > 1):
filename = fname + '.jpg'
plt.savefig(filename)
### Function to convert color
def convert_color(img, conv='RGB2YUV'):
if conv == 'RGB2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
if conv == 'RGB2YUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
if conv == 'RGB2LUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
# Define a function to return HOG features and visualization
### While using YUV, set transform_sqrt=False
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=False,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=False,
visualise=vis, feature_vector=feature_vec)
return features
# Define a function to compute binned color features
def bin_spatial(img, size=(32, 32)):
# Use cv2.resize().ravel() to create the feature vector
features = cv2.resize(img, size).ravel()
# Return the feature vector
if DBG_PRINT == 1:
print('Spatial features:', features.dtype)
return features
# Define a function to compute color histogram features
# NEED TO CHANGE bins_range if reading .png files with mpimg!
def color_hist(img, nbins=32, bins_range=(0, 256)):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
### Plot the hog feature images
def plot_hog_features (img_file, color_space='RGB',
orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
):
# Read in the image
image = mpimg.imread(img_file)
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
fimg = feature_image[:,:,hog_channel]
features, hog_img = get_hog_features(fimg,
orient, pix_per_cell, cell_per_block,
vis=True, feature_vec=True)
return features, feature_image, hog_img
### Extract ALL features from images (Spatial, Color hist, HOG)
def extract_all_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors
features = []
# Iterate through the list of images
for img_file in imgs:
# Create a list for iamge features
img_features = []
# Read in each one by one
img = mpimg.imread(img_file)
# Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append Spatial features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append Color features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append HOG features to list
img_features.append(hog_features)
features.append(np.concatenate(img_features))
#9) Return concatenated array of features
return features
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
### Extract ALL features from Single Image
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
#4) Append features to list
img_features.append(spatial_features)
#print('Spatial:', spatial_features.dtype)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
#6) Append features to list
img_features.append(hist_features)
#print('Color Hist:', hist_features.dtype)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#print('HOG:', hog_features.dtype)
#9) Return concatenated array of features
#print('Img_Features:', img_features[0].dtype,
# img_features[1].dtype, img_features[2].dtype)
return np.concatenate(img_features)
# Define a function to pass an image
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=9,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
#6) Predict using your classifier
prediction = clf.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, cspace, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, \
cell_per_block, spatial_size, hist_bins):
draw_img = np.copy(img)
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
# apply color conversion if other than 'RGB'
#ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YUV')
if cspace != 'RGB':
if cspace == 'HSV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)
elif cspace == 'LUV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)
elif cspace == 'HLS':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)
elif cspace == 'YUV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
elif cspace == 'YCrCb':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
else: ctrans_tosearch = np.copy(image)
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
#print('nblocks_per_window: ', nblocks_per_window)
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
on_windows = []
bboxes = []
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
test_prediction = svc.predict(test_features)
# Enumerate the bounding boxes
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
bboxes.append(((int(xbox_left), int(ytop_draw+ystart)),(int(xbox_left+win_draw),int(ytop_draw+win_draw+ystart))))
if test_prediction == 1:
#xbox_left = np.int(xleft*scale)
#ytop_draw = np.int(ytop*scale)
#win_draw = np.int(window*scale)
#cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
on_windows.append(((int(xbox_left), int(ytop_draw+ystart)),(int(xbox_left+win_draw),int(ytop_draw+win_draw+ystart))))
#print ('1 in_windows: ', on_windows)
return on_windows, bboxes
### Multi-scale sliding window approach
def sliding_windows_multiscale (img, cspace, scale_list, svc, X_scaler, orient, \
pix_per_cell, cell_per_block, spatial_size, hist_bins):
hot_windows = []
search_windows = []
for i in range(0, len(scale_list), 2):
scale = scale_list[i]
(ystart, ystop) = scale_list[i+1]
#print ('scale_window: ', scale, scale_list[i+1])
on_windows, bboxes = find_cars(img, cspace, ystart, ystop, scale, svc, X_scaler, \
orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
#print ('on_windows: ', on_windows)
hot_windows.extend(on_windows)
search_windows.extend(bboxes)
return hot_windows, search_windows
### Heat to map a list of bounding boxes
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def remove_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] -= 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
print ('*** Done ***')
In [3]:
########## Data lookup to create Car & NotCar lists ############################
# Define a function to return some characteristics of the dataset
def data_look(car_list, notcar_list):
data_dict = {}
# Define a key in data_dict "n_cars" and store the number of car images
data_dict["n_cars"] = len(car_list)
# Define a key "n_notcars" and store the number of notcar images
data_dict["n_notcars"] = len(notcar_list)
# Read in a test image, either car or notcar
image = mpimg.imread(car_list[0])
# Define a key "image_shape" and store the test image shape 3-tuple
data_dict["image_shape"] = image.shape
# Define a key "data_type" and store the data type of the test image.
data_dict["data_type"] = image.dtype
# Return data_dict
return data_dict
train_images = glob.glob(data_folder+'*.png')
cars = []
notcars = []
for image in train_images:
if 'non-vehicles' in image:
notcars.append(image)
else:
cars.append(image)
data_info = data_look(cars, notcars)
print('Training Data stats:', data_info["n_cars"], ' cars and', data_info["n_notcars"], ' non-cars')
print('of size: ',data_info["image_shape"], ' and data type:', data_info["data_type"])
# Plot a random car / not-car indices and plot example images
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
# Read in car / not-car images
car_image = mpimg.imread(cars[car_ind])
notcar_image = mpimg.imread(notcars[notcar_ind])
print (car_image.dtype)
# Plot the examples
img_list =[car_image, notcar_image]
title_list = ['Example Car Image', 'Example Not-car Image']
cmap_gray =[0,0]
plot_images(img_list, cmap_gray, title_list, imgs_per_row=2, figsize=(24,9), right = 0.5, \
fname='car_not_car')
print ("***Done***")
In [4]:
# Generate a random index to look at a car image
ind = np.random.randint(0, len(cars))
# Read in the image
image = mpimg.imread(cars[ind])
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Call function with vis=True to see an image output
color_space = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
#y_start_stop = [450, None] # Min and max in y to search in slide_window()
#t=time.time()
img_list = []
cmap_gray = []
title_list = []
# Plot a random car / not-car indices and plot example images
car_ind = np.random.randint(0, len(cars))
notcar_ind = np.random.randint(0, len(notcars))
print ('car_ind: ', car_ind, 'notcar_ind: ', notcar_ind)
# Read in car / not-car images
imgs = []
imgs.append(cars[car_ind])
imgs.append(notcars[notcar_ind])
fimgs = []
for idx,img_file in enumerate(imgs):
# Extract HOG features
features, feature_img, hog_img = plot_hog_features(img_file, color_space=color_space,
orient=9, pix_per_cell=8, cell_per_block=2, hog_channel=0)
img_list.append(np.copy(feature_img[:,:,0]))
cmap_gray.append(1)
if idx == 0:
title_list.append('Car CH-1')
else:
title_list.append('Not Car CH-1')
img_list.append(np.copy(hog_img))
cmap_gray.append(1)
if idx == 0:
title_list.append('Car CH-1 HOG')
else:
title_list.append('Not Car CH-1 HOG')
fimgs.append(feature_img)
# Create the Feature images for each color channel
for channel in range(fimgs[0].shape[2]):
for idx,fimg in enumerate(fimgs):
# Create the Feature Images
img_list.append(np.copy(fimg[:,:,channel]))
cmap_gray.append(1)
if idx == 0: title = 'Car CH - ' + str(channel+1)
else: title = 'Not Car CH - ' + str(channel+1)
title_list.append(title)
# Append the Feature images
img_list.append(np.copy(cv2.resize(fimg[:,:,channel], spatial_size)))
cmap_gray.append(1)
if idx == 0: title = 'Car CH - ' + str(channel+1) + ' Features'
else: title = 'Not Car CH - ' + str(channel+1) + ' Features'
title_list.append(title)
# Plot the Channel/Feature images
plot_images(img_list, cmap_gray, title_list, top=0.8, right=1.0, \
imgs_per_row=4, figsize=(24,32), fontsize=30, fname='HOG_example')
print ('***Done ***')
In [5]:
###### Training SVM Clasifier #####################################################
### Parameter features
# Call function with vis=True to see an image output
color_space = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG eatures on or off
In [84]:
###### Training SVM Clasifier #####################################################
# Reduce the sample size because HOG features are slow to compute
# The quiz evaluator times out after 13s of CPU time
DBG_LIMIT_SAMPLE_SIZE = False
if DBG_LIMIT_SAMPLE_SIZE == True:
sample_size = 500
cars = cars[0:sample_size]
notcars = notcars[0:sample_size]
print ('# of Car samples: ', len(cars), '# of NotCar Samples: ', len(notcars))
t=time.time()
car_features = extract_all_features(cars, color_space=color_space, spatial_size=spatial_size,
hist_bins=hist_bins, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel,
spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_all_features(notcars, color_space=color_space, spatial_size=spatial_size,
hist_bins=hist_bins, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel,
spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG features...')
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.2, random_state=rand_state)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
# Save the Trained classifier
print('\nSaving the SVC Training classifier data to svc_classifier.p')
svc_data = [svc, X_scaler]
pickle.dump(svc_data, open("svc_classifier.p", "wb"))
print ('*** Done***')
In [9]:
### Search for Cars using SLiding Window Approach ####################################
### Read a test image to search for cars
image = mpimg.imread(test_img_folder+'test5.jpg')
draw_image = np.copy(image)
DBG_SCALE = 0
# Define Scale and Window list for multi-scale window search
scount = 80
slist = [1.0, 1.0, 1.5, 1.5, 2.0, 2.0, 3.0, 3.0]
wslist = [400, 420, 400, 440, 400, 480, 380, 460]
scwin_list = []
for i, scale in enumerate(slist):
scwin_list.append(scale)
wstart = wslist[i]
wend = np.int(wslist[i] + scount*scale)
scwin_list.append((wstart, wend))
print ('Scale_List: ', scwin_list)
if DBG_SCALE == 1:
n = 6
scale_list = scwin_list[2*n:2*n+2]
else:
scale_list = scwin_list
print (scale_list)
# Load the Trained classifier
print('*** Loading the SVC Training classifier data to svc_classifier')
svc, X_scaler = pickle.load(open("svc_classifier.p", "rb"))
DBG_CHECK = 0
if DBG_CHECK == 1:
scale = 2.5
ystart, ystop = 380, 380+200
# Search using the sliding window method extracting features(HOG, Spatial..) in each image
on_windows, bboxes = find_cars (image, color_space, ystart, ystop, scale, svc, X_scaler, orient, \
pix_per_cell, cell_per_block, spatial_size, hist_bins)
else:
on_windows, bboxes = sliding_windows_multiscale (image, color_space, scale_list, svc, X_scaler, orient, \
pix_per_cell, cell_per_block, spatial_size, hist_bins)
out_img = draw_boxes(draw_image, on_windows, color=(0, 0, 255), thick=6)
window_img = draw_boxes(draw_image, bboxes, color=(0, 0, 255), thick=6)
# Plot the images
img_list =[window_img]
title_list = ['Bounding boxes Image']
cmap_gray =[0]
plot_images(img_list, cmap_gray, title_list, imgs_per_row=1, figsize=(24,8), \
fname='sliding_windows')
In [10]:
### Read a test image to search for cars
test_imgs = glob.glob(test_img_folder+'*.jpg')
# Apply Sliding window approach on the Test Images
img_list = []
cmap_gray = []
title_list = []
for img_file in test_imgs:
image = mpimg.imread(img_file)
draw_image = np.copy(image)
on_windows, bboxes = sliding_windows_multiscale (image, color_space, scale_list, svc, X_scaler, orient, \
pix_per_cell, cell_per_block, spatial_size, hist_bins)
out_img = draw_boxes(draw_image, on_windows, color=(0, 0, 255), thick=6)
#window_img = draw_boxes(draw_image, bboxes, color=(0, 0, 255), thick=6)
img_list.append(np.copy(out_img))
title_list.append(img_file)
cmap_gray.append(0)
# Plot the examples
#img_list =[window_img, out_img]
#title_list = ['Bounding boxes Image', 'On_Window Image']
#cmap_gray =[0,0]
plot_images(img_list, cmap_gray, title_list, top=0.7, imgs_per_row=2, \
figsize=(24,24), fontsize=30, fname='car_detections')
In [11]:
###### Adding HEAT MAP to the bounding box detections ##########################
# Apply Sliding window approach on the Test Images
img_list = []
cmap_gray = []
title_list = []
limg_list = []
lcmap_gray = []
ltitle_list = []
### Read a test image to search for cars
test_imgs = glob.glob(test_img_folder+'*.jpg')
test_image = test_img_folder+'test5.jpg'
#test_imgs = [test_image]
HEAT_THRESHOLD = 1
for img_file in test_imgs:
image = mpimg.imread(img_file)
draw_image = np.copy(image)
on_windows, bboxes = sliding_windows_multiscale (image, color_space,scale_list, svc, X_scaler, orient, \
pix_per_cell, cell_per_block, spatial_size, hist_bins)
heat = np.zeros_like(image[:,:,0]).astype(np.float)
# Add heat to each box in box list
heat = add_heat(heat, on_windows)
# Apply threshold to help remove false positives
heat = apply_threshold(heat,HEAT_THRESHOLD)
# Visualize the heatmap when displaying
heatmap = np.clip(heat, 0, 255)
# Find final boxes from heatmap using label function
labels = label(heatmap)
pos_img = draw_labeled_bboxes(draw_image, labels)
# Append images for plotting
img_list.append(pos_img)
title_list.append(img_file + ' Car Positions')
cmap_gray.append(0)
img_list.append(np.copy(heatmap))
title_list.append(img_file + ' Heat Map')
cmap_gray.append(2)
# Label Images
limg_list.append(labels[0])
ltitle_list.append(img_file + ' Labels: ' + str(labels[1]))
lcmap_gray.append(1)
# Plot the Bounding boxes and Heat maps
plot_images(img_list, cmap_gray, title_list, top=0.8, imgs_per_row=2,\
figsize=(24,54), fontsize=25, fname='bboxes_and_heat')
# Plot the label maps
plot_images(limg_list, lcmap_gray, ltitle_list, top=0.8, imgs_per_row=1,\
figsize=(24,48), fontsize=20, fname='Labels')
In [12]:
### Process the Video
# Apply Sliding window approach on the Test Images
img_list = []
cmap_gray = []
title_list = []
fnumber = 0
# Load the Trained classifier
print('*** Loading the SVC Training classifier data to svc_classifier')
svc, X_scaler = pickle.load(open("svc_classifier.p", "rb"))
### Class to store history of detections
class Car_Detect():
def __init__(self, nframes):
# Store previous car detections
self.windows = []
self.fnumber = 0
self.store_frames = nframes
self.first_window = None
def update_windows (self, windows):
self.first_window = None
if len(windows) > 0:
self.windows.append(windows)
if len(self.windows) > self.store_frames:
self.first_window = self.windows[0]
self.windows = self.windows[1:]
else: # Once no windows are found remove history
self.windows = []
def update_fnumber (self):
self.fnumber = self.fnumber + 1
### Set the detection type and frame number for plotting
# Define a class to store data from previous frames
store_for_frames = 6
DETECT_TYPE = 0
fstart = 0
fend = 0
def process_image(image):
#cdet.update_fnumber()
#print (cdet.fnumber)
draw_image = np.copy(image)
on_windows, bboxes = sliding_windows_multiscale (image, color_space,scale_list, svc, X_scaler, orient, \
pix_per_cell, cell_per_block, spatial_size, hist_bins)
if DETECT_TYPE == 1:
heat = np.zeros_like(image[:,:,0]).astype(np.float)
# Add heat to each box in box list
heat = add_heat(heat, on_windows)
# Apply threshold to help remove false positives
heat = apply_threshold(heat,1)
# Visualize the heatmap when displaying
heatmap = np.clip(heat, 0, 255)
# Find final boxes from heatmap using label function
labels = label(heatmap)
pos_image = draw_labeled_bboxes(draw_image, labels)
elif DETECT_TYPE == 2:
# add detections to the history
heat = np.zeros_like(image[:,:,0]).astype(np.float)
if len(on_windows) > 0:
cdet.update_windows(on_windows)
for window in cdet.windows:
heat = add_heat(heat, window)
heat = apply_threshold(heat, 1 + len(cdet.windows)//2)
heatmap = np.clip(heat, 0, 255)
labels = label(heatmap)
pos_img = draw_labeled_bboxes(draw_image, labels)
else:
heat = np.zeros_like(image[:,:,0]).astype(np.float)
cdet.update_windows(on_windows)
#if cdet.first_window is not None:
# heat = remove_heat(heat, cdet.first_window)
for window in cdet.windows:
heat = add_heat(heat, window)
heat = apply_threshold(heat, 1 + len(cdet.windows)//2)
heatmap = np.clip(heat, 0, 255)
labels = label(heatmap)
pos_img = draw_labeled_bboxes(draw_image, labels)
'''
# Append images for plotting
if cdet.fnumber >= fstart and cdet.fnumber <= fend:
img_file = 'img' + str(cdet.fnumber)
print ('Frame #: ', cdet.fnumber)
window_img = draw_boxes(draw_image, on_windows, color=(0, 0, 255), thick=6)
img_list.append(np.copy(window_img))
title_list.append(img_file + ' Bounding boxes')
cmap_gray.append(0)
img_list.append(np.copy(heatmap))
title_list.append(img_file + ' Heat Map')
cmap_gray.append(2)
if cdet.fnumber == fend:
# Plot the bounding boxes and heatmap images
plot_images(img_list, cmap_gray, title_list, top=0.8, imgs_per_row=2,\
figsize=(24,48), fontsize=25, fname='bboxes_and_heat')
# Plot the Labels image
limg_list = [np.copy(labels[0])]
title = img_file + ' Labels Map'
ltitle_list = [title]
lcmap_gray = [1]
plot_images(limg_list, lcmap_gray, ltitle_list, top=0.8, imgs_per_row=1,\
figsize=(24,18), fontsize=25, fname='labels_map')
# Plot the Labels image
oimg_list = [np.copy(pos_img)]
title = img_file + ' Labels Map'
otitle_list = [title]
ocmap_gray = [0]
plot_images(oimg_list, ocmap_gray, otitle_list, top=0.8, imgs_per_row=1,\
figsize=(24,18), fontsize=25, fname='output_bboxes')
'''
return pos_img
In [13]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
cdet = Car_Detect(store_for_frames)
TEST_VIDEO = 0
if TEST_VIDEO == 1:
test_out_file = 'test_video_out.mp4'
clip_test = VideoFileClip('test_video.mp4')
else:
test_out_file = 'project_video_out.mp4'
clip_test = VideoFileClip('project_video.mp4')
clip_test_out = clip_test.fl_image(process_image)
%time clip_test_out.write_videofile(test_out_file, audio=False)